1. Field of the Invention
This invention relates to a method for simulating in a lithographic process, and more particularly to a method for simulating in a lithographic process in which semiconductor device manufacturing process, light crystal diode (LCD) manufacturing process, and lithography (electron beam, or X-ray for example) are used.
2. Description of the Prior Art
Even though simulation technology of an optical lithography process had been developed posterior to the time that optical lithography was adopted in the semiconductor industry, its technology has been the main means by which it is necessary to reduce trial and error in a unit-process or new development, and to shorten its development term, since the efficiency is excellent in: the optimization of cell layout, the expectancy of optimized conditions in a complicated process, or in the fast treatment of a large amount of data.
An optical proximity effect correction(OPC) is essential to optimize a mask layout. It is the most common method used for performing optical proximity effect correction. When used, the mask layout is corrected so that the aerial image closely approaches the desired target layout where an aerial image intensity contour is regarded as the top view of a resist pattern.
This method is widely used because it provides more efficient and easy usage; however, there is a disadvantage in that the exactitude falls in the surrounding of resolution limit, and when patterns, whose type, size, and density differ from one another, exist on a mask with various shapes, it is very difficult to expect a patterning status while only the aerial image is used.
It should be noted that a proximity effect in the lithography process has optical and non-optical components. Since the OPC, generally, is the proximity effect correction for a full lithography process, it is not satisfied with the correction of the aerial image (i.e., pure optical component) and so the proximity effect by the resist process (i.e., the non-optical component) should be considered.
While the OPC including the resist process is most desirable in this field, the full process OPC is not usually considered since the resist process simulation belongs to difficult fields. Instead, in order to increase the exactitude of the OPC, a method taking proper proximity to the full process has actively been studied.
A proximate simulation method, which can obtain comparatively exact results without simulating the full process, and the results when this method is applied to a 1GD OPC, will be explained hereinafter.
There are various manners of simulating the lithography process and then analyzing the result thereof. One of them is a manner where full courses of the lithography process are simulated. The courses are broadly composed of 3 steps: 1) step for calculating the aerial image, 2) step for calling a latent image, 3) step for calculating a development course. (Refer to FIG. 1)
The aerial image is defined as an intensity distribution of light just prior to reaching the resist on a surface of a wafer, while the wafer is exposed as the mask is equipped in an exposure apparatus. In order to simulate the aerial image, a layout onto the mask and an exposure condition of the exposure apparatus (for instance, NA: Numerical Aperture, .sigma.: Partial Coherence Factor) should be required as input parameters.
In the case that the resist is exposed by the aerial image, there are complications where each partial amount of light that is reflected from the surface, is absorbed onto the resist, or is reflected from a surface of the wafer substrate and then returned back to the resist. At this moment, the latent image, i.e., the intensity distribution of light which is absorbed onto the resist after exposure, is generated. Not only these resist characteristics (for instance, refractive index : Dill's A,B,C) but also parameters (for instance, albed, refractive index) representing the characteristics of the wafer substrate should be included in input parameters for simulating the latent image.
In the case of a chemical amplification resist, a photo acid generator (PAG) is resolved by energy absorbed in the resist, and acid is then produced. Next, the acid is passed through a diffusion course by a post exposure bake (PEB) which is the next process, then a non-protection group and then another acid is produced in response to a protection group. Subsequently, similar series of courses in which the acid is reacted to other non-protection group occur, and such a course is expressed as an amplification in the present invention. Thus, the non-protection group is finally dissolved by a developing solution and thereby forming the resist pattern.
In order to simulate a resist profile in such a manner, the PEB parameters (for instance, temperature, time) and development parameters (for instance, development speed, type and concentration of developing solution) should be provided therein.
A program for calculating the aerial image in commercial software has been well developed and a program for simulating the resist profile has been also developed. However, while the aerial image simulation is established based on an obvious theory, the resist process simulation is formed almost entirely on models and many input parameters are required so as to simulate the full cours. Accordingly, the full process simulation is not used much yet, since there are still disadvantages in that a long simulation time is required and the exactitude is not exact because a resist parameter is not known except for a minimum factor.
Accordingly, the major method used, instead of the full process simulation, is a manner of performing only the aerial image simulation and then expecting the experimental results using a threshold model. Here, the threshold model is an analysis method that more or less values, centering around any intensity (i.e., the threshold value) among intensity distribution of light obtained by the result of the aerial image simulation, determining an existence/non-existence of the pattern.
Namely, with this method, in a case of positive photo resist, the resist is dissolved by the developing solution and then exhausted in all at over the threshold value, while the resist is not dissolved by the developing solution and remains as a pattern. Thus, there is an advantage in that it is very simple and it does not take much time. In this manner, the energy level and pattern size (exactly, the aerial image size) are known by a threshold cutting of the aerial image.
FIG. 2 shows dense lines of 0.25 .mu.m L/S, isolated lines I/L, and the intensity of the aerial image of isolated space I/S. In case that a threshold level is defined as 0.25 in a normalized intensity as indicated in FIG. 2, the exposure energy is in inverse proportion to the intensity. In this case, exposure mount is 4 times than the threshold exposure energy E.sub.th. Thus, each dense line L/S, isolated line I/L, and a pattern size of the isolated space I/S belongs to image sizes: W.sub.L/S (.apprxeq.0.20 .mu.m), W.sub.I/L (.apprxeq.0.25 .mu.m), and W.sub.I/S (.apprxeq.0.30 .mu.m), respectively.
In a case of such threshold modeling, when a variety of the exposure energy is low or a defocus value is not high in a type of pattern, it is relatively possible to expect the exact expectancy. However, the in case that either the exposure energy is varied in a wide space, the defocus value is high, or the pattern's type and size vary, the expectancy differs with the experimental results. As described above, as it is very difficult for the threshold model of the aerial image to explain its expectancy of the experimental results in general circumstances, it has been widely used in this field due to the advantages that 1) only the calculation of the aerial image is enable to expect the experimental result, 2) usage manner is easy in comparison to the full process simulation since the model itself is very simplified, 3) the expectancy of the experimental results in a unsophisticated status is obtained within a short time.
Under these circumstances, the threshold model should have shown discrepancy with respect to the experimental results in order to explain the full process including the resist process by using the aerial image only. Further, the full process simulation serves little assistance in the optimization of the actual processes, since there is a burden that the speed is slow and a lot of input parameters must be inputted exactly.